Next-Gen Predictive Maintenance System Using AI for Automotive Fleets

Our SME in the automotive industry is dedicated to enhancing fleet management through cutting-edge technology. We are looking to develop a predictive maintenance system that can accurately forecast maintenance needs using AI. The system will utilize computer vision to monitor vehicle conditions in real-time and employ predictive analytics to analyze historic data, identifying patterns that precede mechanical failures. The system will be implemented using TensorFlow for building machine learning models, OpenAI API for data processing, and integration with existing fleet management software. The project will include setting up data pipelines using Langchain and Pinecone to handle vast amounts of vehicle data efficiently. The ideal solution will not only predict maintenance needs but also provide actionable insights, helping fleet operators schedule proactive servicing. By reducing unexpected breakdowns and optimizing maintenance schedules, this system will significantly cut costs and improve vehicle uptime, providing a competitive edge in fleet operations. The project will span 12-16 weeks, allowing for rigorous testing and refinement to ensure robustness and accuracy.